In the cooking game [1]}, the player is located in a house, which contains multiple rooms and interactable objects (food, tools, etc.). Her / his task is to follow the recipe to prepare the meal. Each game instance has a unique recipe, including different numbers of ingredients (food objects that are necessary for preparing the meal) and their corresponding preparation requirements (e.g., “slice”, “fry”). Besides the textual observation, the KG-based observation can also be directly obtained from the environment. The game sets used in our work contains a task set \(\mathcal {T}\)  of 268 subtasks, and an action set \(\mathcal {A}\)  of 1304 actions. Following GATA's experiment setting [2]}, we simplify the game environment by making the action set changeable over time, which can be provided by the TextWorld platform. Note that although the action space is reduced, it still remains challenging as the agent may encounter unseen action candidates [3]}, [4]}. We then use a similar way to obtain a changeable task set, which is a combination of the verb set \(\lbrace \text{chop, dice, slice, fry, make, get, grill, roast}\rbrace \)  and the ingredient set, where the construction details are provided in Appendix . Table REF  and Table REF  show the KG-based observations \(o_t\) , corresponding subtask candidates \(\mathcal {T}\)  and action candidates \(\mathcal {A}\) . Table REF  and Table REF  show more examples of subtasks and actions, respectively. The underlined subtask candidates denote the available subtask set \(\mathcal {T}_t\) . The underlined action candidates in Table REF  denote the refined action set \(\mathcal {A}_t\)  after selecting the subtask “roast carrot”. We still denote the subtask candidate set (action candidate set) as \(\mathcal {T}\)  (\(\mathcal {A}\) ) to distinguish it from the available subtask set \(\mathcal {T}_t\)  (refined action set \(\mathcal {A}_t\) ).
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